def train(type='ALL_CORPS', start_no=1):
"""하나의 세션으로 학습시키는 기본 모델 """
corps = get_corps()
params = TrainParams(type)
invests = LearningNMockInvestment(params)
invests.let_train_invests(corps, start_no)
def recommend_corps(recommend_month: str, train_model: str = 'rnn') -> None:
"""하나의 세션으로 학습시키는 기본 모델 """
month = DateUtils.to_date(recommend_month, '%Y.%m')
params = GlobalParams(train_model=train_model)
#params.remove_session_file = True
before_month_start = DateUtils.to_month_str(month,
-params.mock_period_months)
before_month_end = DateUtils.to_month_str(month, -1)
params.invest_start_date = before_month_start + '.01'
params.invest_end_date = DateUtils.to_date_str(month -
datetime.timedelta(days=1))
params.result_file_name = "MOCK_" + before_month_start + "-" + before_month_end
corp = Corp(params)
corps = corp.get_eval_corps_auto(params.invest_end_date)
invests = LearningNMockInvestment(params)
invests.train_n_invests(corps)
before_result = pd.read_csv(invests.get_result_file_path())
if params.rmse_max_recommend is not None:
before_result = before_result.query("rmse<" +
str(params.rmse_max_recommend))
before_result = before_result.sort_values(by='invest_result',
ascending=False)
before_result.index = range(len(before_result.index))
save_file_name = "recommend_months_" + recommend_month + ".xlsx"
save_file_path = os.path.join('result', train_model, save_file_name)
DataUtils.save_csv(before_result, save_file_path)
print(before_result)
def train_codes(corp_codes, train_model='rnn'):
"""하나의 세션으로 학습시키는 기본 모델 """
corp = Corp(type)
corps = corp.get_corps_for_codes(corp_codes)
params = GlobalParams(train_model=train_model)
invests = LearningNMockInvestment(params)
invests.train_n_invests(corps)
def train_one(corp_name='카카오', train_model='rnn'):
"""하나의 세션으로 학습시키는 기본 모델 """
corp = Corp(type)
corps = corp.get_corp(corp_name)
params = GlobalParams(train_model=train_model)
invests = LearningNMockInvestment(params)
invests.train_n_invests(corps)
def twins(start_no=1):
corp = Corp()
corps = corp.get_eval_corps()
params = TrainParams("EACH")
params.result_file_name = 'twins_result'
invests = LearningNMockInvestment(params)
invests.let_train_invests_twins(corps, start_no)
def train_all_corps(type='ALL_CORPS', start_no=1):
"""하나의 세션으로 모든 회사를 학습시킨다. """
corp = Corp()
corps = corp.get_corps()
params = TrainParams(type)
params.result_file_name = "training_" + type.lower() + "_result"
invests = LearningNMockInvestment(params)
invests.let_train_invests(corps, start_no)
def train(type: str = 'EACH',
start_no: int = 1,
train_model: str = 'rnn',
invest_only: bool = False) -> None:
"""하나의 세션으로 학습시키는 기본 모델 """
corps = get_corps(type)
params = GlobalParams(type, train_model=train_model)
invests = LearningNMockInvestment(params)
invests.train_n_invests(corps, start_no, invest_only)
def main(corp_names: list = None,
train_model: str = 'rnn',
invest_only: bool = False) -> None:
if corp_names is None:
corp_names = ["에이치엘비", "동일철강", "키다리스튜디오"]
params = GlobalParams('FORCAST', train_model=train_model)
invests = LearningNMockInvestment(params)
invests.train_n_invests_for_name(corp_names, invest_only)
def main(corp_names=None):
if corp_names is None:
corp_names = [
"삼성중공업", "기아자동차", "게임빌", "루트로닉", "영진약품", "대아티아이", "한국타이어"
]
#corp_names = ["JYP Ent.", "KTH"]
params = TrainParams('FORCAST')
invests = LearningNMockInvestment(params)
invests.let_train_invests_for_name(corp_names)
def train_n_invests_top10(self, corps):
""" 상위 10개를 사는 방법으로 모의투자를 실행한다. """
learning_invest = LearningNMockInvestment(self.params)
invest_count = self.params.invest_count
invest_data = []
for i in range(invest_count):
j = 0
for index, corp_data in corps.iterrows():
if i == 0:
invest_row = self.train_top10(i, j, corp_data)
invest_data.append(invest_row)
else:
invest_row = invest_data[j]
self.train_top10(i, j, corp_data, invest_row)
#print(i, j, invest_row)
j += 1
self.invest_top10(i, invest_data)
print(invest_data)
self.sell_all_stock(invest_data)
print("final", invest_data)
df_invest_data = pd.DataFrame(invest_data, columns=self.RESULT_COLUMNS)
DataUtils.save_excel(df_invest_data,
learning_invest.get_result_file_path())
def train_top10(self, i, j, corp_data, invest_row=None):
""" top10 모의투자 방법을 위하여 학습을 시킨다."""
learning_invest = LearningNMockInvestment(self.params)
invest = MockInvestment(self.params)
invest_count = self.params.invest_count
if invest_row is None:
corp_code = corp_data['종목코드']
else:
corp_code = invest_row[1]
stocks = Stocks(self.params)
stock_data = stocks.get_stock_data(corp_code)
stock_data_now = stock_data[:i - invest_count]
rmse_val, train_cnt, data_params, dataX_last, scaler_close = learning_invest.train(
corp_code, stock_data_now)
last_money, last_predict, invest_predicts, all_invest_money = invest.invest(
corp_code, dataX_last, data_params)
last_close_money, last_pred_money = invest.get_real_money(
data_params, scaler_close, last_predict)
last_pred_ratio = (last_pred_money -
last_close_money) / last_close_money * 100
if invest_row is None:
corp_name = corp_data['회사명']
all_invest_money, all_stock_count = invest.buy_stock(
self.params.invest_money, last_close_money, 0)
invest_row = [
j, corp_code, corp_name, last_pred_ratio, last_close_money, 0,
0, all_invest_money, all_stock_count, rmse_val
]
else:
#print(invest_row)
invest_row[3] = last_pred_ratio
invest_row[4] = last_close_money
return invest_row
def train_months(start: str = '2018.01',
end: str = '2018.09',
invest_money: float = 100000000,
train_model: str = 'rnn') -> None:
"""하나의 세션으로 학습시키는 기본 모델 """
start_month = DateUtils.to_date(start, '%Y.%m')
end_month = DateUtils.to_date(end, '%Y.%m')
between = DateUtils.between_months(start_month, end_month)
invest_months_result = []
result_columns = ["month", "invest_money", "result_money"]
MOCK_MONEY = 10000000
chart_data = []
params = None
index_money = None
for i in range(between + 1):
params = GlobalParams(train_model=train_model)
#params.remove_session_file = True
before_month_start = DateUtils.to_month_str(
start_month, i - params.mock_period_months)
before_month_end = DateUtils.to_month_str(start_month, i - 1)
params.invest_start_date = before_month_start + '.01'
params.invest_end_date = before_month_end + '.31'
params.result_file_name = "MOCK_" + before_month_start + "-" + before_month_end
params.invest_money = MOCK_MONEY
corp = Corp(params)
corps = corp.get_eval_corps_auto(params.invest_end_date)
invests = LearningNMockInvestment(params)
invests.train_n_invests(corps)
before_result = pd.read_csv(invests.get_result_file_path())
now_month = DateUtils.to_month_str(start_month, i)
if params.rmse_max_recommend is not None:
before_result = before_result.query("rmse<" +
str(params.rmse_max_recommend))
before_result = corp.exclude_corps(before_result, now_month)
before_result = before_result.sort_values(by='invest_result',
ascending=False)
before_result.index = range(len(before_result.index))
corp10_codes = before_result.loc[:9, 'code']
corp10_codes.index = range(len(corp10_codes.index))
corp10 = corp.get_corps_for_codes(corp10_codes)
corp10_len = len(corp10_codes.index)
params = GlobalParams(train_model=train_model)
#params.remove_session_file = False
params.invest_start_date = now_month + '.01'
params.invest_end_date = now_month + '.31'
params.result_file_name = "INVEST_" + now_month
params.invest_money = invest_money / corp10_len
if index_money is not None:
params.index_money = index_money / corp10_len
invests = LearningNMockInvestment(params)
invest_chart_data = invests.train_n_invests(corp10, invest_only=False)
chart_data.append(invest_chart_data)
now_result = pd.read_csv(invests.get_result_file_path())
invest_money = now_result['invest_result'].sum()
index_money = now_result['all_invest_result'].sum()
invest_months_result.append(
[now_month, params.invest_money * corp10_len, invest_money])
print(now_month, params.invest_money * corp10_len, invest_money)
df_imr = pd.DataFrame(invest_months_result, columns=result_columns)
save_file_name = "recommend_months_" + start + "-" + end + ".xlsx"
if "_" in train_model:
save_file_path = os.path.join('result', train_model,
params.ensemble_type, save_file_name)
else:
save_file_path = os.path.join('result', train_model, save_file_name)
DataUtils.save_csv(df_imr, save_file_path)
if len(chart_data) > 1 and params is not None:
visualizer = InvestVisualizer(params)
visualizer.draw_invest_months(chart_data, start, end)
print()
def forcasts(corps_n_date: list) -> None:
params = GlobalParams('FORCAST')
invests = LearningNMockInvestment(params)
invests.forcasts(corps_n_date)